Content teams are often caught between oversimplified metrics and impossibly complex attribution models. Deborah Carver, creator of The Content Technologist, has spent over a decade helping organizations find the sweet spot in between — where measurement actually drives better content decisions.
Key insights from this interview:
- Content measurement requires a more sophisticated model than just pageviews, but simpler than perfect attribution.
- The most overlooked metrics are often around retention and churn.
- Your measurement strategy should be in place before creating content.
- Organic traffic often drives your highest-quality customers, but few teams have the analytics setup and know-how to discover this.
In this interview, Deborah breaks down how to effectively use Google Analytics 4 for content measurement, shares her framework for tracking the customer journey from awareness to retention, and explains why content teams need to own their analytics rather than relying on others to measure their impact.
Editor's note: This interview has been lightly edited for clarity and readability.
Tim: Can you share your career journey and what led you to your current role?
Deborah: My career started in book publishing, then went to B2B publishing in the early 2010s. I was at a B2B trade publication for caterers, where I was making all their content — their print magazine, website, and email content. I was really excited about Instagram and social media because it was for caterers, so little tiny foods were obviously a natural fit.
I had no idea what was working though. I knew Google Analytics existed but hadn't done any of the trainings or understood what was important to look for. So I switched jobs and went to an agency focused on performance marketing, where I was hired as an SEO content specialist. Everyone had to be certified in Google Analytics — that was a requirement. Back then the certification was much more intensive than now, requiring you to know things like regular expressions.
We learned Google Analytics from some very smart, curious people. It was a magical time in the early 2010s with many super smarties working together to solve digital marketing problems.
Then I moved to an agency that was part of a media company, where they didn't really have much performance measurement in place. I built out their digital strategy and analytics capability there. I've now been working independently for five years, but one of my strengths has always been that Google Analytics background and understanding exactly what you can find in the tool and different methodologies for approaching measurement.
What are your thoughts on attribution and trying to understand exactly what convinces someone to make a purchase or visit a website?
Attribution is chasing the dragon. You're never going to understand exactly what convinces someone to purchase or visit your website. You can't predict human behavior or know what's going on in anyone's head. We can make guesses, but even though we're doing this all on these giant calculators — computers are just calculators — it's still never gonna match up perfectly.
It's more productive to understand the larger picture: when we produce this much content, we get this kind of effect; when we change the messaging, we get that kind of effect. My degree is in mass communications and media effects, so it's always about knowing that this thing affects people kind of, and being okay with that uncertainty is as good as you're gonna get.
Attribution is much more about understanding how people are getting to your brand and what you can do to affect their perception of your brand. It's less precise than trying to create a perfect attribution model — that's over-engineering the whole thing.
There seems to be a spectrum between trying to have perfect attribution and simply looking at page views. Where should content marketers aim to be on this spectrum?
There's definitely a level of sophistication in between. Content people tend to be allergic to math, which I understand — I didn't get into this business to do math either. But the math you need is usually early high school level, like how to read a chart and understand which metrics mean what.
It's about creating a more sophisticated mental model for how you can measure human behavior besides just views. Understanding a more sophisticated model is important.
Do you have guidance on which attribution model makes sense for content?
Generally, a consumer journey would be awareness, engagement, conversion. I look at visibility, awareness, engagement, commitment (which is generally a newsletter signup or a content signup), and then retention — which people forget about, even though that is historically what content marketing has been for.
It's about understanding what role your content plays in all those cycles, and each stage has two or three metrics you can track. It's about understanding that full cycle, that your reader is sophisticated, and there's not one single inflection point that's actually going to push them to purchase. Content is always a long game.
One big thing that people can do in Google Analytics is to stop looking at daily metrics and instead look at changes month over month or quarter over quarter.
What are the most overlooked metrics by content marketers that you think are important?
Retention is extremely overlooked, especially in rapid growth. Churn is absolutely overlooked, and the number one way to prevent customers from churning is to have good content that keeps them engaged and keeps them subscribed.
People don't realize that customer service content is immensely important to keep your customers engaged, to keep them understanding all the changes that you're making to the product. I hire and recommend B2B SaaS based on the quality of their customer service content.
Can you explain the concept of what you call "hidden gem metrics" and provide some examples?
Hidden gem metrics are about looking at how traffic compares with engagement. Looking at channel dimensions is really important - even if you don't set up your own custom channel dimensions, understanding how organic performs versus paid is key. Paid advertising tools will take credit for as much as they possibly can, but you need to understand how organic compares in terms of time on site and returning users.
Another really important general KPI I look for is returning direct visitors — these are people going directly to your homepage or website. They're often overlooked because people are so focused on platform distribution.
While distribution is important for brand growth, returning direct visitors are the people who already care and want to consume your content regularly. If you don't have anything on your website to tell them what content is new and how often it's updated, though, you're not going to have very high returning direct visitors.
How do organic content and paid advertising work together, and what insights have you gained from analyzing their performance?
Organic and paid work together, but there's always been a tiny bit of competition as far as budget goes because paid media budgets are so huge and organic content budgets are not.
And it's hilarious because when you actually analyze it and look at what's happening: yes, it all works together, but users that come in from organic channels are more committed. They spend more time on site and are more engaged.
If you're only investing in paid media, you're only going to see paid media conversions, inflection points, and paid media performance. But if you're actually investing in organic content, you'll find that organic is usually the sticking point for your best customers. I say this from having looked at hundreds of Google Analytics accounts over the past 11 years.
What are some key features or metrics in Google Analytics 4 that you find particularly useful for content analysis?
The number one thing that people can do in Google Analytics 4 is set up what they're now calling your key events — what used to be conversions. It's about understanding your key commitment points, whether that's someone signing up for a newsletter, filling out a contact form, or spending more than a minute on your website.
For content, those commitment points are different than for paid advertising With content, you want people to spend more time on site. GA4 now allows you to customize your reports to see the average engagement time per user and per session, as well as the engagement rate more generally.
In addition to setting up key events and tracking, understanding what media is driving your highest quality customers is crucial.
It's more important to understand the actual metrics in the tool and decide what you want to measure ahead of time, rather than just looking at the standard data without a clear goal in mind.
Content grouping is also a useful feature, but it requires some Google Tag Manager setup.
Can you explain how key events work in Google Analytics? Do you need to connect them to Google Tags, or can you set them up directly in Google Analytics?
You can set up key events directly in Google Analytics using enhanced measurement. Enhanced measurement reads your site and automatically tags events like form submits, video views, or search results. While this automated approach can capture your most important events, it's crucial to double-check that it's tracking correctly.
Even if you don't fine-tune the setup, turning on enhanced measurement will at least get you started with tracking those events. You can then go into the admin settings and decide which events should be considered key events.
How important is it to have a measurement strategy in place before creating content?
Having a measurement strategy is essential. It's all about knowing what the most important metrics you're tracking are. If you create a piece of content, you should have specific goals in mind, such as growing your brand, attracting new audiences, or inspiring purchases.
Setting up your key events and conversions ahead of time allows you to measure the before and after effects of your content. I'm a big fan of benchmarking — take your temperature where you're at, then see what happens after you launch new content. Understanding your averages and regular performance is crucial for evaluating the impact of different campaigns or content pieces.
Can AI be helpful in understanding and analyzing content metrics?
It can definitely help to an extent. You can tell the AI, "Explain it to me like I'm a fifth grader." But there's a part of measurement that is just straight-up learning the menu. You have to sit down and memorize what the metrics mean. Then you'll do a much better job of understanding what the data means as a whole.
I find that automated insights and AI-generated insights in analytics platforms are usually pretty rudimentary and don't tell a full story. The machine learning is not necessarily getting better as it goes on and as more people are using it.
Trust your own brain before you ask the computer. There's no substitute for experience and just sitting down and doing the work.
What final advice do you have for content marketers when it comes to measurement and analytics?
Don't be frightened of measurement. The more that content marketers and content people can learn about measurement, the more they can advocate for themselves. No one else is going to measure content properly because they don't know how and they are not trained to do so.
When you do start measuring your content yourself, you'll see that you're often the most impactful in the organization. Understanding the fundamentals, memorizing the menu, understanding the whole picture of what could be in a measurement tool — it's not that big, it's doable.
Putting the time into learning it will help you understand your job more. It'll definitely help your salary go up and it will help you advocate for your content better.
Deborah Carver is the creator of The Content Technologist, a consultancy and newsletter focused on content strategy and technology. With over 20 years of experience spanning traditional publishing and digital marketing, she helps organizations develop content strategies and measurement frameworks that drive better content decisions.
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